The ethical integration of Artificial Intelligence (AI) in healthcare necessitates addressing fairness-a concept that is highly context-specific across medical fields. Extensive studies have been conducted to expand the technical components of AI fairness, while tremendous calls for AI fairness have been raised from healthcare. Despite this, a significant disconnect persists between technical advancements and their practical clinical applications, resulting in a lack of contextualized discussion of AI fairness in clinical settings. Through a detailed evidence gap analysis, our review systematically pinpoints several deficiencies concerning both healthcare data and the provided AI fairness solutions. We highlight the scarcity of research on AI fairness in many medical domains where AI technology is increasingly utilized. Additionally, our analysis highlights a substantial reliance on group fairness, aiming to ensure equality among demographic groups from a macro healthcare system perspective; in contrast, individual fairness, focusing on equity at a more granular level, is frequently overlooked. To bridge these gaps, our review advances actionable strategies for both the healthcare and AI research communities. Beyond applying existing AI fairness methods in healthcare, we further emphasize the importance of involving healthcare professionals to refine AI fairness concepts and methods to ensure contextually relevant and ethically sound AI applications in healthcare.

翻译：人工智能在医疗领域的伦理整合必须解决公平性问题——这一概念在不同医学领域中具有高度情境特异性。已有大量研究致力于拓展人工智能公平性的技术要素，同时医疗界对人工智能公平性的呼声日益高涨。尽管如此，技术进展与实际临床应用之间仍存在显著脱节，导致临床环境中缺乏情境化的人工智能公平性讨论。通过细致的证据缺口分析，本综述系统性地指出了医疗数据与现有人工智能公平性解决方案存在的若干不足。我们强调，在人工智能技术日益普及的众多医学领域中，针对人工智能公平性的研究仍显匮乏。此外，分析表明现有研究过度依赖群体公平性——即从宏观医疗体系视角确保不同人口群体的平等性；相比之下，关注更微观层面公平性的个体公平性则常被忽视。为弥合这些差距，本综述为医疗界和人工智能研究界提出了可实施的策略。除了将现有人工智能公平性方法应用于医疗领域外，我们进一步强调医疗专业人员参与优化人工智能公平性概念与方法的重要性，以确保医疗领域人工智能应用既符合情境需求又遵循伦理规范。